Decision Trees with Hypotheses

Decision Trees with Hypotheses

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Abstract / Description

  • Presents the concept of a hypothesis about the values of all attributes

  • Provides tools for the experimental and theoretical study of decision trees with hypotheses

  • Compares these decision trees with conventional decision trees that use only queries, each based on a single attribute

In this book, the concept of a hypothesis about the values of all attributes is added to the standard decision tree model, considered, in particular, in test theory and rough set theory. This extension allows us to use the analog of equivalence queries from exact learning and explore decision trees that are based on various combinations of attributes, hypotheses, and proper hypotheses (analog of proper equivalence queries). The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute.

Both experimental and theoretical results show that decision trees with hypotheses can have less complexity than conventional decision trees. These results open up some prospects for using decision trees with hypotheses as a means of knowledge representation and algorithms for computing Boolean functions. The obtained theoretical results and tools for studying decision trees with hypotheses are useful for researchers using decision trees and rules in data analysis. This book can also be used as the basis for graduate courses.

Table of Contents

Front Matter

Decision Tables

Binary Information Systems and Infinite Families of Concepts

Back Matter

Publication Date

11-19-2022

Faculty / School

School of Mathematics and Computer Science (SMCS)

Department

Department of Computer Science

Was this content written or created while at IBA?

Yes

Series

Synthesis Lectures on Intelligent Technologies

Author Affiliation

  • Dr. Shahid Hussain is Assistant Professor and Chairperson Computer Science

e-ISBN/e-ISSN

978-3-031-08585-7

Rights Information

The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Decision Trees with Hypotheses

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